The np Package - NexTag Supports Open Source Initiatives

npudens 35npudensKernel Density and Distribution Estimation with Mixed Data TypesDescriptionUsagenpudens computes kernel unconditional density estimates on evaluation data, given a set of trainingdata and a bandwidth specification (a bandwidth object or a bandwidth vector, bandwidthtype, and kernel type) using the method of Li and Racine (2003). Similarly npudist computeskernel unconditional cumulative distribution estimates.npudens(bws = stop(paste("bandwidths are required to perform the estimate!","please set 'bws'")), ...)## S3 method for class 'formula':npudens(bws, data = NULL, newdata = NULL, ...)## S3 method for class 'bandwidth':npudens(bws,tdat = stop("invoked without training data 'tdat'"),edat,...)## S3 method for class 'call':npudens(bws, ...)## Default S3 method:npudens(bws,tdat = stop("invoked without training data 'tdat'"),edat,...)Argumentsbwsa bandwidth specification. This can be set as a bandwidth object returnedfrom an invocation of npudensbw, or as a p-vector of bandwidths, with anelement for each variable in the training data. If specified as a vector, thenadditional arguments will need to be supplied as necessary to change them fromthe defaults to specify the bandwidth type, kernel types, training data, and so on.... additional arguments supplied to specify, the training data, the bandwidth type,kernel types, and so on. This is necessary if you specify bws as a p-vector andnot a bandwidth object, and you do not desire the default behaviours. To dothis, you may specify any of bwscaling, bwtype, ckertype, ckerorder,ukertype, okertype, as described in npudensbw.tdata p-variate data frame of sample realizations (training data) used to estimate thedensity. Defaults to the training data used to compute the bandwidth object.